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This application made use of a Multiple Neural Network Learning System (MNNLS) to replicate the decisions made by mortgage insurance underwriters. The MNNLS was trained on previous underwriter judgements and learned to mimic their underwriting skills. The system reached a high degree of agreement with human underwriters when testing on previously unseen examples. Disagreements were examined using case studies, a single feature distribution analysis and a quality analysis. These studies indicate that human underwriters in many cases disagree with one another and are inconsistent in the use of their underwriting guidelines. It was found that when the MNNLS system and the underwriter disagree, the system's classifications are more consistent with the guidelines than the underwriter's judgement.

The present paper describes various kinds of surgery carried out with great success in 16 cases which included both severe and moderate haemophilia patients with modest amounts of factor concentrates and anti-fibrinolytic drugs. This is very important in developing countries where factor concentrates are not easily available. In one patient haemophilia was diagnosed only after surgery. None of the patients had inhibitor pre- or post-operatively. One patient who was HIV positive underwent orchidectomy successfully with only 6000 IU of factor VIII concentrate.

In the event of disasters such as hurricanes, earthquakes and terrorism, emergency relief supplies need be distributed to disaster victims in timely manner to protect the health and lives of the victims. We develop a modeling framework for disaster response where the supply chain of relief supplies and distribution operations are simulated, and analytics for the optimal transportation of relief supplies to various POD (Points of Distribution) are tested. Our simulation model of disaster response includes modeling the supply chain of relief supplies, distribution operations at PODs, dynamics of demand, and progression of disaster. Our analytics optimize the dispatch of relief supplies to PODs and cross-leveling among PODs. Their effectiveness is estimated by the simulation model. The model can evaluate a wide range of disaster scenarios, assess existing disaster response plans and policies, and identify better approaches for government agencies and first responders to prepare for and respond to disasters.

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Design considerations for robustness with respect to variations and low power operations typically impose contradictory design requirements. Low power design techniques such as voltage scaling, dual-Vth etc. can have a large negative impact on parametric yield. In this paper, we propose a novel paradigm for low-power variationtolerant circuit design, which allows aggressive voltage scaling. The principal idea is to (a) isolate and predict the set ofpossible paths that may become critical under process variations, (b) ensure that they are activated rarely, and (c) avoid possible delay failures in the critical paths by dynamically switching to two-cycle operation (assuming all standard operations are single cycle), when they are activated. This allows us to operate the circuit at reduced supply voltage while achieving the required yield. Simulation results on a set of benchmark circuits at 70nm process technology show average power reduction of 60% with less than 10% performance overhead and 18% overhead in die-area compared to conventional synthesis. Application of the proposed methodology to pipelined design is also investigated.